ADEPT

Stat 365: Statistical Communication

Monday, May 15th

Today we will…

  • ADEPT
  • Quarto + pdf
  • ggplot customization
  • Copy the Master’s work time

The ASCCR Framework for Collaboration

ASCCR: Communication



ADEPT: Explaining Statistical Ideas

  • Anology
  • Diagram
  • Example
  • Plain Language
  • Technical Definition

Analogy

  • “A comparison between two things, typically on the basis of their structure and for the purpose of explanation or clarification.”

  • Connect the statistical idea to an idea that the audience likely already understands

  • Relating the fundamental components of the idea.

Analogy

Analogy

Diagram

  • Pictures can more easily demonstrate the idea.

  • Make sure the diagram is thoughtfully created.

Example

  • Select a simple example, in a context that is relevant to the audiences expertise or field.

  • Then build upon it

Plain language

  • Avoid statistical jargon and words with double meanings (e.g., confidence, error, etc.)

  • Use language relevant to the audience’s field

  • Be concise.

  • Explain at the level that meets the audience’s needs.

Technical Definition

  • Only if such a definition will serve the audience’s needs

  • Understand how the audience will be using the technical definition

  • Use statistical notation carefully

Analogies to consider

Brainstorm!

Which statistical idea do you think this image might relate to?

Statistical Idea Analogy
Type I error In a jury trial, convicting an innocent person
Type II error In a jury trial, letting a guilty person go free

Brainstorm!

Which statistical idea do you think this image might relate to?

Statistical Idea Analogy
Sample vs Census Taking a blood sample at your health check-up

Let’s talk about Quarto & ggplot

Quarto + pdf

install.packages("tinytex")
tinytex::install_tinytex()

Example1

Include a graphic in your Quarto document

knitr::include_graphics("casselman-feature-marriage-3.webp")

Data Management

library(tidyverse)
library(lubridate)
library(ggthemes)
women_orig <- read_csv("women.csv")

women_clean <- women_orig |> 
  select(year, date, kids_HS_2534, kids_BAp_2534, nokids_HS_2534, nokids_BAp_2534) |> 
  pivot_longer(cols = c("kids_HS_2534", 
                        "kids_BAp_2534", 
                        "nokids_HS_2534", 
                        "nokids_BAp_2534"),
               names_to = "group",
               values_to = "marriage_rate"
               ) |> 
  separate_wider_delim(cols = "group", 
                       names = c("kids", "education", NA), 
                       delim = "_"
                       ) |> 
  mutate(marriage_rate = 1 - marriage_rate)
head(women_clean)
# A tibble: 6 × 5
   year date       kids   education marriage_rate
  <dbl> <date>     <chr>  <chr>             <dbl>
1  1960 1960-01-01 kids   HS                0.996
2  1960 1960-01-01 kids   BAp               0.998
3  1960 1960-01-01 nokids HS                0.608
4  1960 1960-01-01 nokids BAp               0.426
5  1970 1970-01-01 kids   HS                0.982
6  1970 1970-01-01 kids   BAp               0.995

First Draft

Code 
women_clean |> 
  ggplot(aes(x = date, 
             y = marriage_rate)
         ) +
  geom_line(aes(color = education),
            ) +
  facet_wrap(~ kids) +
  scale_y_continuous(limits = c(0,1))

Colors1

Code 
women_clean |> 
  ggplot(aes(x = date, 
             y = marriage_rate)
         ) +
  geom_line(aes(color = education),
            linewidth = 1
            ) +
  facet_wrap(~ kids) +
  scale_y_continuous(limits = c(0,1)) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c"))

Axis Labels

Code 
women_clean |> 
  ggplot(aes(x = date, 
             y = marriage_rate)
         ) +
  geom_line(aes(color = education),
            linewidth = 1
            ) +
  facet_wrap(~ kids) +
  scale_y_continuous(limits = c(0,1),
                     labels = scales::percent,
                     breaks = seq(0,1,0.25)
                     ) +
  scale_x_date(limits = c(mdy("01-01-1960"), 
                          mdy("01-01-2012")
                          ),
               breaks = c(mdy("01-01-1960"), 
                          mdy("01-01-1970"), 
                          mdy("01-01-1980"),
                          mdy("01-01-1990"),
                          mdy("01-01-2000"),
                          mdy("01-01-2010")
                          ),
               date_labels = c("%Y", "%y", "%y", "%y", "%Y", "%y")) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c")) +
  theme(axis.title = element_blank())

annotate + geom_text() + library(ggrepel)

Code 
women_clean |> 
  ggplot(aes(x = date, y = marriage_rate)) +
  geom_line(aes(color = education),
            linewidth = 1) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "High school or less"),
           x = mdy("01-01-1982"),
           y = 0.6
           ) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "College graduates"),
           x = mdy("01-01-1995"), 
           y = 1.05,
           ) +
  facet_wrap(~ kids) +
  scale_y_continuous(limits = c(0,1.1),
                     labels = scales::percent,
                     breaks = seq(0,1,0.25)
                     ) +
  scale_x_date(limits = c(mdy("01-01-1960"), 
                          mdy("01-01-2012")
                          ),
               breaks = c(mdy("01-01-1960"), 
                          mdy("01-01-1970"), 
                          mdy("01-01-1980"),
                          mdy("01-01-1990"),
                          mdy("01-01-2000"),
                          mdy("01-01-2010")
                          ),
               date_labels = c("%Y", "%y", "%y", "%y", "%Y", "%y")) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c")) +
  theme(axis.title = element_blank(),
        legend.position = "none")

facet labels

Code 
women_clean |> 
  ggplot(aes(x = date, y = marriage_rate)) +
  geom_line(aes(color = education),
            linewidth = 1) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "High school or less"),
           x = mdy("01-01-1982"),
           y = 0.6
           ) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "College graduates"),
           x = mdy("01-01-1995"), 
           y = 1.05,
           ) +
  facet_wrap(~ kids,
             labeller = labeller(kids = c(`kids` = "WITH CHILDREN", 
                                          `nokids` = "WITHOUT CHILDREN")
                                 )
             ) +
  scale_y_continuous(limits = c(0,1.1),
                     labels = scales::percent,
                     breaks = seq(0,1,0.25)
                     ) +
  scale_x_date(limits = c(mdy("01-01-1960"), 
                          mdy("01-01-2012")
                          ),
               breaks = c(mdy("01-01-1960"), 
                          mdy("01-01-1970"), 
                          mdy("01-01-1980"),
                          mdy("01-01-1990"),
                          mdy("01-01-2000"),
                          mdy("01-01-2010")
                          ),
               date_labels = c("%Y", "%y", "%y", "%y", "%Y", "%y")) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c")) +
  theme(axis.title = element_blank(),
        legend.position = "none")

Themes

Code 
women_clean |> 
  ggplot(aes(x = date, y = marriage_rate)) +
  geom_line(aes(color = education),
            linewidth = 1) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "High school or less"),
           x = mdy("01-01-1982"),
           y = 0.6
           ) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "College graduates"),
           x = mdy("01-01-1995"), 
           y = 1.05,
           ) +
  facet_wrap(~ kids,
             labeller = labeller(kids = c(`kids` = "WITH CHILDREN", 
                                          `nokids` = "WITHOUT CHILDREN")
                                 )
             ) +
  scale_y_continuous(limits = c(0,1.1),
                     labels = scales::percent,
                     breaks = seq(0,1,0.25)
                     ) +
  scale_x_date(limits = c(mdy("01-01-1960"), 
                          mdy("01-01-2012")
                          ),
               breaks = c(mdy("01-01-1960"), 
                          mdy("01-01-1970"), 
                          mdy("01-01-1980"),
                          mdy("01-01-1990"),
                          mdy("01-01-2000"),
                          mdy("01-01-2010")
                          ),
               date_labels = c("%Y", "%y", "%y", "%y", "%Y", "%y")) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_blank(),
        legend.position = "none")

Titles

Code 
women_clean |> 
  ggplot(aes(x = date, y = marriage_rate)) +
  geom_line(aes(color = education),
            linewidth = 1) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "High school or less"),
           x = mdy("01-01-1982"),
           y = 0.6
           ) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "College graduates"),
           x = mdy("01-01-1995"), 
           y = 1.05,
           ) +
  facet_wrap(~ kids,
             labeller = labeller(kids = c(`kids` = "WITH CHILDREN", 
                                          `nokids` = "WITHOUT CHILDREN")
                                 )
             ) +
  scale_y_continuous(limits = c(0,1.1),
                     labels = scales::percent,
                     breaks = seq(0,1,0.25)
                     ) +
  scale_x_date(limits = c(mdy("01-01-1960"), 
                          mdy("01-01-2012")
                          ),
               breaks = c(mdy("01-01-1960"), 
                          mdy("01-01-1970"), 
                          mdy("01-01-1980"),
                          mdy("01-01-1990"),
                          mdy("01-01-2000"),
                          mdy("01-01-2010")
                          ),
               date_labels = c("%Y", "%y", "%y", "%y", "%Y", "%y")) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_blank(),
        legend.position = "none") +
  labs(title = "Marriage Rates For Women By Education",
       subtitle = "Ages 25 to 35")

Final Touches

Code 
women_clean |> 
  ggplot(aes(x = date, y = marriage_rate)) +
  geom_line(aes(color = education),
            linewidth = 1) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "High school or less"),
           x = mdy("01-01-1982"),
           y = 0.6,
           color = "gray30"
           ) +
  geom_segment(data = tibble(kids = "kids"),
               aes(x = mdy("01-01-1982"),
                   xend = mdy("01-01-1982"),
                   y = 0.65,
                   yend = 0.94
                   )
               ) +
  geom_text(data = tibble(kids = "kids"),
           aes(label = "College graduates"),
           x = mdy("01-01-1995"), 
           y = 1.05,
           color = "gray30"
           ) +
  geom_segment(data = tibble(kids = "kids"),
               aes(x = mdy("01-01-2003"),
                   xend = mdy("01-01-2003"),
                   y = 1,
                   yend = 0.94
                   )
               ) +
  facet_wrap(~ kids,
             labeller = labeller(kids = c(`kids` = "WITH CHILDREN", 
                                          `nokids` = "WITHOUT CHILDREN")
                                 )
             ) +
  scale_y_continuous(limits = c(0,1.1),
                     labels = c(0, 25, 50, 75, "100%"),
                     breaks = seq(0,1,0.25)
                     ) +
  scale_x_date(limits = c(mdy("01-01-1960"), 
                          mdy("01-01-2012")
                          ),
               breaks = c(mdy("01-01-1960"), 
                          mdy("01-01-1970"), 
                          mdy("01-01-1980"),
                          mdy("01-01-1990"),
                          mdy("01-01-2000"),
                          mdy("01-01-2010")
                          ),
               date_labels = c("%Y", "'%y", "'%y", "'%y", "%Y", "'%y")) +
  scale_color_manual(values = c("#8c6bb1", "#810f7c")) +
  theme_fivethirtyeight() +
  theme(axis.title = element_blank(),
        legend.position = "none",
        strip.text = element_text(face = "bold")) +
  labs(title = "Marriage Rates For Women By Education",
       subtitle = "Ages 25 to 35",
       caption = "BASED ON CENSUS BUREAU DATA") +
  geom_hline(yintercept = 0)

Other useful tips!

  • Stick plots together with library(patchwork)
library(patchwork)

p1 + p2

p1/p2
  • Use HTML tags to add color to titles and/or labels
library(ggtext)

ggplot() +
  ... +
  labs(title = "Your title with <span style = 'color:#004400'>Colored Text</span>") +
  theme(plot.title   = element_markdown())
  • or create breaks in titles with <br>

Copy the Master’s!

To do

Copy the Masters: First Draft

  • due Thursday, 5/18 at 11:59pm

Draft: Results and Discussion

  • due Thursday, 5/18 at 11:59pm